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Gemma 4 is the clearest alternative family to evaluate: compare its 26B-A4B and 31B models with Qwen3.8-27B, and consider Gemma 4 12B when you want more memory headroom. None is a guaranteed fit or universal winner on every 24GB GPU. Quantization, context length, inference runtime and other GPU memory use determine whether a particular setup works.
Which models should you compare?
These are candidates, not a controlled ranking. Google DeepMind publishes results for Gemma 4 on specific tasks, while Qwen publishes its own results for Qwen3.8-27B. The figures come from separate model pages and do not establish which model performs best under identical local hardware, prompts, quantization and runtime.
| Candidate | Why consider it | Published evidence | What to verify for 24GB |
|---|---|---|---|
| Gemma 4 26B-A4B | A Google-positioned efficient, consumer-GPU candidate for reasoning and coding comparisons. | Google DeepMind reports 88.3% on AIME 2026 and 77.1% on LiveCodeBench v6 for Gemma 4 26B A4B IT Thinking. Source: Google DeepMind. | The reviewed official page does not specify a quantization and context configuration that guarantees a fit on a 24GB GPU. |
| Gemma 4 31B | A larger Gemma option for comparing task performance. | Google DeepMind reports 89.2% on AIME 2026 and 80.0% on LiveCodeBench v6 for Gemma 4 31B IT Thinking. Source: Google DeepMind. | Google’s consumer-GPU positioning is not a promise that this model fits a particular 24GB card, quantization or context length. |
| Gemma 4 12B | A smaller family option to consider when memory headroom or deployment simplicity matters. | Google lists Gemma 4 12B among its offerings. Exact comparable scores are not stated on the reviewed page. Source: Google DeepMind. | The reviewed material does not give an exact 24GB deployment recipe or a fair head-to-head against Qwen3.8-27B. |
| Qwen3.6-27B | A useful previous-generation baseline if you already use Qwen. | Qwen’s Qwen3.8-27B model card lists Qwen3.6-27B at 63.4 on Terminal-Bench 2.1 and 53.5 on SWE-bench Pro, alongside Qwen3.8-27B results. Source: Qwen model card. | The reviewed material does not establish its exact local memory use. |
For a useful comparison, run the same representative tasks with each candidate using the same GPU, inference engine, quantization and context length. Include your actual tool calls and modalities; benchmark scores alone do not predict how a model will handle your workflow.
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Will Qwen3.8-27B fit on a 24GB GPU?
It can be a viable target, but “24GB GPU” is not a complete fit specification. The Qwen repository describes Qwen3.8-27B as a 27B causal language model with a vision encoder, 64 layers and a native 262,144-token context, extendable up to 1,000,000 tokens. Those context capabilities do not mean a 24GB card can serve the model at those lengths. Qwen model card.
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Weights are only part of the memory budget. Context and runtime overhead also consume GPU memory, and the available headroom depends on what else is using the card. A third-party estimate puts Q4_K_M weights at about 16.4GB and total use at about 19GB at 8K context. It identifies 24GB cards such as the RTX 3090 as a comfortable class for that specific estimate; it is not a guarantee across runtimes, quantizations or longer contexts. CanItRun’s Qwen3.8-27B estimate.
AMD’s August 14, 2026 article says roughly 24GB of VGM or VRAM is needed to run Qwen3.8-27B comfortably in LM Studio on supported AMD systems. The article also reports preliminary results under specified Windows and Vulkan configurations; those results do not establish performance or fit on other GPUs. AMD’s setup and preliminary results.
Check the complete configuration
- GPU and available memory: Record the exact card and how much VRAM is free before loading the model.
- Quantization: Check the actual quantized model file and its memory needs; a model name alone does not identify its deployment size.
- Context length: Plan for the context you will actually use. A short-context estimate cannot guarantee fit at a much longer context.
- Runtime and workload: Memory use varies by inference engine, settings and whether you load vision or other capabilities.
Apply these checks to Gemma 4 as well. The reviewed Google page describes efficient or consumer-GPU-oriented models but does not publish an exact quantization-and-context recipe that guarantees any of the listed variants fit every 24GB card.
How do the published scores compare?
Use scores to identify questions worth testing, not to declare an overall local winner. On Google DeepMind’s page, Gemma 4 31B IT Thinking scores 89.2% on AIME 2026 and 80.0% on LiveCodeBench v6; Gemma 4 26B A4B IT Thinking scores 88.3% and 77.1% on those same listed tasks. Google DeepMind’s results.
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Qwen’s 2026 model card reports 89.2 on GPQA Diamond, 73.0 on Terminal-Bench 2.1 and 61.7 on SWE-bench Pro for Qwen3.8-27B. It lists 63.4 on Terminal-Bench 2.1 and 53.5 on SWE-bench Pro for Qwen3.6-27B. These results are reported by Qwen, and the Gemma figures come from Google DeepMind; differing benchmark tasks and separate publisher pages prevent a complete apples-to-apples ranking. Qwen model card.
For your own decision, match tests to the job: coding tasks, agent or terminal workflows, image or video understanding, or reasoning prompts. Record failures and quality as well as speed and memory use. A benchmark result does not guarantee the same outcome on your prompts or local setup.
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Which local runtime should you use?
Qwen lists compatibility with Transformers, vLLM and SGLang, among other serving and inference formats. AMD describes LM Studio and Lemonade paths for supported systems. These are compatibility options, not a guarantee that every combination of operating system, GPU backend and model format is currently supported. Check the model and runtime documentation for your exact platform before downloading a quantized build or planning deployment. Qwen model card; AMD setup article.
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What else should you check before choosing?
- Modality: Qwen’s model card describes image and video understanding. Confirm that a Gemma or Qwen deployment supports the inputs you need in your chosen runtime.
- Reasoning controls: Qwen documents thinking-mode and reasoning-effort controls. Check how your selected model and inference stack expose comparable settings.
- License and use: Review the current official terms for the exact model before commercial use or redistribution. The terms are not established here, so do not assume candidates share the same conditions.
- Actual task quality: Test representative prompts and tool workflows rather than inferring your result from a vendor score.
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